Elbow method in python

Asked Aug 06 '18 at 08:41

Active Dec 14 '18 at 01:25

Viewed 2,095 times

i am trying to implement the elbow method in python on my own to get the optimum number of clusters. Therefore i summed the inertia's of the different k-means runs:

sum_squared_dist = []
K = range(1,30)
for k in K:
    km = KMeans(n_clusters=k, random_state=0)
    km = km.fit(normalized_modeling_data)
    sum_squared_dist.append(km.inertia_)
plt.plot(K, sum_squared_dist, 'bx-')
plt.xlabel('number of clusters k')
plt.ylabel('Sum of squared distances')
plt.show

So the next approach would be to find the point, were the curve starts to flatten (which should mean that the first derivation is falling). Is there an built-in method in numpy or scikit-learn to calculate the derivation from an array?

asked Aug 06 '18 at 08:41

ItFreak

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Are you looking for [`numpy.diff`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.diff.html) ? – Sasha Tsukanov Aug 06 '18 at 08:47
thank you :) did not thought of looking for something like difference – ItFreak Aug 06 '18 at 08:52
Maybe you are looking for this? https://stackoverflow.com/questions/2018178/finding-the-best-trade-off-point-on-a-curve – ching.lin Aug 06 '18 at 09:41

Elbow method in python

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